The operation of many mechanical systems is based on the transformation of angular motions to other kinds of movement. Rotating machinery components play a crucial role in this process and are frequently used in mechanical systems. As such, effectively detecting the conditions of these components can help to reduce repair costs and maintain reliable and productive operations.
The intense loading conditions of bearings and gears together with their harsh operating environment makes them failure-prone. This is particularly true for rolling-element bearings due to their structural delicacy. Bearing failure could lead to the complete stall of a mechanical system, unplanned productivity loss for production facilities or catastrophic consequences for mission-critical equipment.
The failures of rolling-element bearings are often caused by surface faults such as cracks or spalls on bearing rollers or bearing races. Contact between the bearing faults (cracks, spalls, etc) and the mating surfaces (rollers, bearing races, etc) during the operation of the bearing generates impulses. These impulses excite a resonance in the entire system including the bearing and the structure where the bearing is mounted. The resulting high frequency resonance is typically damped out quickly due to the structural damping characteristics of the whole system. This process is repeated periodically due to the recurrence of the contact between the fault and the mating surface.
A conventional approach to detecting bearing faults involves the use of sensors such as accelerometers, vibrometers or other vibration sensors affixed to the machinery being analyzed. Preferably the sensors are placed on, or near the particular bearings under evaluation.
Raw data signals are obtained from the sensors during the operation of the machine. However, the raw data signals contain not only the fault related vibrations but also the background noise present in any measurement device, also known as intrinsic noise signals, as well as the vibrations generated by other mechanical components such as gear meshing, shaft imbalance or misalignment, also known as interference signals.
In addition, the target fault characteristic signals are typically of small amplitude compared to the interference and noise signals present in typical raw data signals. This is depicted in FIGS. 1 and 2. FIG. 1 illustrates a simulated mixture of bearing fault impulses and mechanical vibration interferences. FIG. 2 illustrates a simulated mixture of bearing fault impulses, mechanical vibration interferences and white noise arising from the operation of a normal machine with typical mechanical vibration and electrical noise.
Usually once a sufficient set of raw vibration signal data has been obtained, the raw vibration signal data is analyzed in order to discern or identify the target fault characteristic signal or signatures. In this manner, damaged or faulty machine bearings are identified.
One common approach to the analysis of the sensor raw data is known as the high frequency resonance (“HFR”) approach.
FIG. 3 is a flowchart of a common HFR approach for detecting bearing faults. Raw vibration signal data is obtained from vibration sensors 20, and the band-pass filter parameters are pre-selected 21. The band-pass filter parameters include both the center frequency and the bandwidth for the band-pass filter. Once the band-pass filter parameters are selected, the vibration data signal is band-pass filtered 22. The purpose of step 22 is to ensure that all frequency components out of the range of interest, especially any low frequency large amplitude interference vibrations, have been removed from the signal. Step 22 is also necessary because amplitude demodulation of raw vibration signal data contaminated by low frequency high amplitude interference is typically ineffective for detecting bearing faults.
Amplitude demodulation is applied to the band-pass filtered signal 23, followed by spectral analysis in the form of a conventional Fast Fourier Transform of the amplitude demodulated signal 24. The Fast Fourier transformed signal is analyzed 25 to detect the target fault characteristic frequency and its harmonics. If the target fault characteristic frequency and its harmonics are detected 26, an alert signal is generated 27, otherwise the method is repeated using new raw vibration signal data until the target fault characteristic frequency and its harmonics are detected.
One major disadvantage of the HFR approach is the need to pre-select the band-pass filter parameters 21. This step presupposes significant advance knowledge of the system, including the relevant resonance frequencies and the frequency bandwidth associated with the resonance frequencies forming the frequency band of interest. If these parameters are inaccurately specified, the desired target fault characteristic signal can be reduced, distorted, or even filtered out, i.e. rejected along with the noise and interference. Therefore, optimal implementation of the HFR approach requires accurate foreknowledge of the band-pass filter parameters.
Of concern, the pre-specification of optimal band-pass filter parameters can be difficult, expensive, time consuming and sometimes impossible. These parameters can be affected by numerous variables including the bearing resonance frequencies, resonant frequencies and other vibrational characteristics of the machine and structure where the bearing is mounted. As a result, there is often uncertainty in the optimal value of the band-pass filter parameters.
Even in situations when the optimal band-pass filter parameters are known in principle, problems can still be encountered during actual machine operation, since the machine conditions can change over time. This can occur, for example, given changes in machine temperature, pressure, general machine wear, operating speed, loads, and other factors.
As a result, optimal implementation of the HFR approach requires that the band-pass filter parameters be re-selected in response to the changing machine conditions. Re-selection of the parameters is also necessary when components of the same machine are modified, or different mechanical systems are tested.
However, re-selection of the band-pass filter parameters is difficult and time consuming, since the machine conditions can change in a rapid and unpredictable manner. There is also a possibility that the machine conditions change in the process of the analysis and even before the new filter parameters have been re-selected.
Given the above factors, band-pass filters used in HFR approaches are often considered to be non-optimal and sufficiently broad so as to accommodate uncertainty in the value of the parameters or drift in the machine operating conditions. This ‘detuning’ ensures that the target fault characteristic signals are not rejected by the band-pass filter. However such a widening of the band-pass filter parameters also admits more noise and interference to the subsequent signal processing steps, thus undermining the usefulness of HFR approaches.
In addition to HFR approaches, there are several other known approaches for detecting bearing faults. However, many of these approaches also require pre-specification of the analysis parameters. The other approaches are briefly listed below, along with some of their fundamental disadvantages.
Attempts have been made to apply Fast-Fourier Transform (“FFT”) directly to the raw vibration signal data However, the Fourier transformed signal is difficult or impossible to interpret in the presence of noise and interference. As a result, this approach is ineffective for on-line (real-time) applications where fast decisions are needed.
Bearing fault detection approaches using statistical indices to process the raw vibration signal data also exist. These approaches often suffer the disadvantages of sensitivity to irrelevant signal components. Such indices are sensitive to random, sporadic interferences and outliers, often causing false-positives or leaving faults undetected. This leads to ambiguity and poor user confidence.
The faults of mechanical components featuring impulsive and/or transient signatures, including but not limited to bearings, gears, journal bearings, slider cranks, cams, shafts, springs and dampers also need to be detected using a method which does not suffer from the deficiencies of the prior art. For example, gear faults typically include pits, chips or cracks. Contact between the fault surfaces and the mating gear tooth typically generates impulsive or transient vibrations. Similar issues arise when using the prior art signal processing techniques as summarized above to extract the fault signatures from bearings as well as other mechanical components.
In addition to the impulsive and transient fault signatures, non-impulsive signatures in the form of various signal modulations are also observed in the vibrations measured from faulty mechanical components. Such signatures are usually attributed to faults with smooth geometry such as wear. For example, gear faults featuring tooth profile distortion can lead to amplitude and phase modulations (AM and PM) of the meshing vibrations. The strength of such modulations increases with the development of faults. Hence, trend analysis on the intensity of modulation components can be effectively used to track the health state of gears.
Most of the current gear fault detection techniques focus either on the faults with smooth geometry, e.g., wear, or on those with sudden tooth profile changes, e.g., cracked or broken teeth, but not both. There is no known method that can simultaneously capture multiple forms of fault features in order to provide more reliable fault detection results. In addition, as the faults may exhibit multiple signatures spreading over a wide frequency band, there are drawbacks to adopting a narrowband strategy as taken in many of the gear fault detection techniques proposed so far, such as the HFR method. Such methods may reject an important signal component corresponding to a specific fault symptom.
Another class of fault detection method is based on a signal that is averaged synchronously with the rotation of the gear. However, this type of method is often ineffective in extracting impulsive fault signatures.
The requirements of using accurate pre-specified analysis parameters in many fault detection approaches of the prior art render them difficult to implement and insufficiently versatile to serve a wide range of applications. Furthermore, these techniques are single-fault-type oriented and are unable to detect multiple faults of different nature. In real-world situations, these disadvantages greatly limit their applicability. A parameter-free and versatile approach to the detection of fault characteristic signals for bearings, gears and other rotational mechanical components is therefore needed.